"""simple docstring"""


import argparse
import json

import requests
import timm
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import AutoImageProcessor, SwinConfig, SwinForImageClassification





def      lowerCamelCase_					(   __lowerCAmelCase							)				->   Optional[int]:




						'''simple docstring'''



						lowerCamelCase__   =SwinConfig()
						lowerCamelCase__   =swin_name.split("_"							)

						lowerCamelCase__   =name_split[1]
						lowerCamelCase__   =int(name_split[4]							)
						lowerCamelCase__   =int(name_split[3][-1]							)

						if model_size == "tiny":
												lowerCamelCase__   =96
												lowerCamelCase__   =(2, 2, 6, 2)
												lowerCamelCase__   =(3, 6, 12, 24)
						elif model_size == "small":
												lowerCamelCase__   =96
												lowerCamelCase__   =(2, 2, 18, 2)
												lowerCamelCase__   =(3, 6, 12, 24)
						elif model_size == "base":
												lowerCamelCase__   =128
												lowerCamelCase__   =(2, 2, 18, 2)
												lowerCamelCase__   =(4, 8, 16, 32)
						else:
												lowerCamelCase__   =192
												lowerCamelCase__   =(2, 2, 18, 2)
												lowerCamelCase__   =(6, 12, 24, 48)

						if "in22k" in swin_name:
												lowerCamelCase__   =2_1841
						else:
												lowerCamelCase__   =1000
												lowerCamelCase__   ="huggingface/label-files"
												lowerCamelCase__   ="imagenet-1k-id2label.json"
												lowerCamelCase__   =json.load(open(hf_hub_download(__lowerCAmelCase  ,       __lowerCAmelCase  ,       repo_type="dataset"							)  ,       "r"							)							)
												lowerCamelCase__   ={int(__lowerCAmelCase							): v for k, v in idalabel.items()}
												lowerCamelCase__   =idalabel
												lowerCamelCase__   ={v: k for k, v in idalabel.items()}

						lowerCamelCase__   =img_size
						lowerCamelCase__   =num_classes
						lowerCamelCase__   =embed_dim
						lowerCamelCase__   =depths
						lowerCamelCase__   =num_heads
						lowerCamelCase__   =window_size

						return config





def      lowerCamelCase_					(   __lowerCAmelCase							)				->   List[str]:




						'''simple docstring'''



						if "patch_embed.proj" in name:
												lowerCamelCase__   =name.replace("patch_embed.proj"  ,       "embeddings.patch_embeddings.projection"							)
						if "patch_embed.norm" in name:
												lowerCamelCase__   =name.replace("patch_embed.norm"  ,       "embeddings.norm"							)
						if "layers" in name:
												lowerCamelCase__   ="encoder." + name
						if "attn.proj" in name:
												lowerCamelCase__   =name.replace("attn.proj"  ,       "attention.output.dense"							)
						if "attn" in name:
												lowerCamelCase__   =name.replace("attn"  ,       "attention.self"							)
						if "norm1" in name:
												lowerCamelCase__   =name.replace("norm1"  ,       "layernorm_before"							)
						if "norm2" in name:
												lowerCamelCase__   =name.replace("norm2"  ,       "layernorm_after"							)
						if "mlp.fc1" in name:
												lowerCamelCase__   =name.replace("mlp.fc1"  ,       "intermediate.dense"							)
						if "mlp.fc2" in name:
												lowerCamelCase__   =name.replace("mlp.fc2"  ,       "output.dense"							)

						if name == "norm.weight":
												lowerCamelCase__   ="layernorm.weight"
						if name == "norm.bias":
												lowerCamelCase__   ="layernorm.bias"

						if "head" in name:
												lowerCamelCase__   =name.replace("head"  ,       "classifier"							)
						else:
												lowerCamelCase__   ="swin." + name

						return name





def      lowerCamelCase_					(   __lowerCAmelCase  ,       __lowerCAmelCase							)				->   Dict:




						'''simple docstring'''



						for key in orig_state_dict.copy().keys():
												lowerCamelCase__   =orig_state_dict.pop(__lowerCAmelCase							)

												if "mask" in key:
																		continue
												elif "qkv" in key:
																		lowerCamelCase__   =key.split("."							)
																		lowerCamelCase__   =int(key_split[1]							)
																		lowerCamelCase__   =int(key_split[3]							)
																		lowerCamelCase__   =model.swin.encoder.layers[layer_num].blocks[block_num].attention.self.all_head_size

																		if "weight" in key:
																								lowerCamelCase__   =val[:dim, :]
																								lowerCamelCase__   =val[
																								    dim : dim * 2, :
																								]
																								lowerCamelCase__   =val[-dim:, :]
																		else:
																								lowerCamelCase__   =val[
																								    :dim
																								]
																								lowerCamelCase__   =val[
																								    dim : dim * 2
																								]
																								lowerCamelCase__   =val[
																								    -dim:
																								]
												else:
																		lowerCamelCase__   =val

						return orig_state_dict





def      lowerCamelCase_					(   __lowerCAmelCase  ,       __lowerCAmelCase							)				->   str:




						'''simple docstring'''



						lowerCamelCase__   =timm.create_model(__lowerCAmelCase  ,       pretrained=__lowerCAmelCase							)
						timm_model.eval()

						lowerCamelCase__   =get_swin_config(__lowerCAmelCase							)
						lowerCamelCase__   =SwinForImageClassification(__lowerCAmelCase							)
						model.eval()

						lowerCamelCase__   =convert_state_dict(timm_model.state_dict()  ,       __lowerCAmelCase							)
						model.load_state_dict(__lowerCAmelCase							)

						lowerCamelCase__   ="http://images.cocodataset.org/val2017/000000039769.jpg"

						lowerCamelCase__   =AutoImageProcessor.from_pretrained("microsoft/{}".format(swin_name.replace("_"  ,       "-"							)							)							)
						lowerCamelCase__   =Image.open(requests.get(__lowerCAmelCase  ,       stream=__lowerCAmelCase							).raw							)
						lowerCamelCase__   =image_processor(images=__lowerCAmelCase  ,       return_tensors="pt"							)

						lowerCamelCase__   =timm_model(inputs["pixel_values"]							)
						lowerCamelCase__   =model(**__lowerCAmelCase							).logits

						assert torch.allclose(__lowerCAmelCase  ,       __lowerCAmelCase  ,       atol=1e-3							)

						print(F'''Saving model {swin_name} to {pytorch_dump_folder_path}'''							)
						model.save_pretrained(__lowerCAmelCase							)

						print(F'''Saving image processor to {pytorch_dump_folder_path}'''							)
						image_processor.save_pretrained(__lowerCAmelCase							)


if __name__ == "__main__":
	a				=argparse.ArgumentParser()
	# Required parameters
	parser.add_argument(
	    '--swin_name',
	    default='swin_tiny_patch4_window7_224',
	    type=str,
	    help='Name of the Swin timm model you\'d like to convert.',
	)
	parser.add_argument(
	    '--pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model directory.'
	)

	a				=parser.parse_args()
	convert_swin_checkpoint(args.swin_name, args.pytorch_dump_folder_path)



"""simple docstring"""


import copy

from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING


a				=logging.get_logger(__name__)

a				={
    'ut/deta': 'https://huggingface.co/ut/deta/resolve/main/config.json',
}






class   __UpperCAmelCase		(      __lowerCAmelCase      ):
				A__ :  str    				=			'''deta'''
				A__ :  List[str]    				=			{
				    '''hidden_size''': '''d_model''',
				    '''num_attention_heads''': '''encoder_attention_heads''',
				}

				def __init__( self      ,					_lowerCamelCase=None      ,					_lowerCamelCase=900      ,					_lowerCamelCase=2048      ,					_lowerCamelCase=6      ,					_lowerCamelCase=2048      ,					_lowerCamelCase=8      ,					_lowerCamelCase=6      ,					_lowerCamelCase=1024      ,					_lowerCamelCase=8      ,					_lowerCamelCase=0.0      ,					_lowerCamelCase=True      ,					_lowerCamelCase="relu"      ,					_lowerCamelCase=256      ,					_lowerCamelCase=0.1      ,					_lowerCamelCase=0.0      ,					_lowerCamelCase=0.0      ,					_lowerCamelCase=0.0_2      ,					_lowerCamelCase=1.0      ,					_lowerCamelCase=True      ,					_lowerCamelCase=False      ,					_lowerCamelCase="sine"      ,					_lowerCamelCase=5      ,					_lowerCamelCase=4      ,					_lowerCamelCase=4      ,					_lowerCamelCase=True      ,					_lowerCamelCase=300      ,					_lowerCamelCase=True      ,					_lowerCamelCase=True      ,					_lowerCamelCase=1      ,					_lowerCamelCase=5      ,					_lowerCamelCase=2      ,					_lowerCamelCase=1      ,					_lowerCamelCase=1      ,					_lowerCamelCase=5      ,					_lowerCamelCase=2      ,					_lowerCamelCase=0.1      ,					_lowerCamelCase=0.2_5      ,					**_lowerCamelCase      ,					):
										if backbone_config is None:
																logger.info("`backbone_config` is `None`. Initializing the config with the default `ResNet` backbone." )
																lowerCamelCase__   =CONFIG_MAPPING["resnet"](out_features=["stage2", "stage3", "stage4"] )
										else:
																if isinstance(_lowerCamelCase      ,					_lowerCamelCase ):
																						lowerCamelCase__   =backbone_config.pop("model_type" )
																						lowerCamelCase__   =CONFIG_MAPPING[backbone_model_type]
																						lowerCamelCase__   =config_class.from_dict(_lowerCamelCase )

										lowerCamelCase__   =backbone_config
										lowerCamelCase__   =num_queries
										lowerCamelCase__   =max_position_embeddings
										lowerCamelCase__   =d_model
										lowerCamelCase__   =encoder_ffn_dim
										lowerCamelCase__   =encoder_layers
										lowerCamelCase__   =encoder_attention_heads
										lowerCamelCase__   =decoder_ffn_dim
										lowerCamelCase__   =decoder_layers
										lowerCamelCase__   =decoder_attention_heads
										lowerCamelCase__   =dropout
										lowerCamelCase__   =attention_dropout
										lowerCamelCase__   =activation_dropout
										lowerCamelCase__   =activation_function
										lowerCamelCase__   =init_std
										lowerCamelCase__   =init_xavier_std
										lowerCamelCase__   =encoder_layerdrop
										lowerCamelCase__   =auxiliary_loss
										lowerCamelCase__   =position_embedding_type
										# deformable attributes
										lowerCamelCase__   =num_feature_levels
										lowerCamelCase__   =encoder_n_points
										lowerCamelCase__   =decoder_n_points
										lowerCamelCase__   =two_stage
										lowerCamelCase__   =two_stage_num_proposals
										lowerCamelCase__   =with_box_refine
										lowerCamelCase__   =assign_first_stage
										if two_stage is True and with_box_refine is False:
																raise ValueError("If two_stage is True, with_box_refine must be True." )
										# Hungarian matcher
										lowerCamelCase__   =class_cost
										lowerCamelCase__   =bbox_cost
										lowerCamelCase__   =giou_cost
										# Loss coefficients
										lowerCamelCase__   =mask_loss_coefficient
										lowerCamelCase__   =dice_loss_coefficient
										lowerCamelCase__   =bbox_loss_coefficient
										lowerCamelCase__   =giou_loss_coefficient
										lowerCamelCase__   =eos_coefficient
										lowerCamelCase__   =focal_alpha
										super().__init__(is_encoder_decoder=_lowerCamelCase      ,					**_lowerCamelCase )

				@property
				def 						_a      ( self ):
										return self.encoder_attention_heads

				@property
				def 						_a      ( self ):
										return self.d_model






				def 						_a      ( self ):
										lowerCamelCase__   =copy.deepcopy(self.__dict__ )
										lowerCamelCase__   =self.backbone_config.to_dict()
										lowerCamelCase__   =self.__class__.model_type
										return output


